That's interesting! Last year we made a data analysis (for German Railway magazine DB mobil) figuring out which cities are most popular when it comes to #interrail photos on Instagram. And the result is pretty similar: http://imgur.com/gallery/kHhSn

This is data from Blinq, a (discontinued) Swiss Tinder-like app about who swipes left or right.

Not surprising: Women (red) in general (but there are notable exceptions), hardly ever swipe right. More surprising: Men (blue) show varying behaviours, from always swiping right (and thus undermining the app) to being equally selective as women (and having low chances for matches).

We grabbed the data from Instagram photos using hashtag #tattoo from January to March 2015 and counted co-occurring hashtags. As simple as that. No deep learning image recognition involved, sorry ;-)

The analysis is part of our book «Nach dem Wochenende bin ich erstmal #krank» which was published in Germany, a collection of data charts about life on Instagram (http://a.co/i6aWkzQ). The design is by Ole Häntzschel, coding by David Goldwich and analysis by Tin Fischer.

Please note: Since Instagram restricted API access in July 2016 we unfortunately can no longer do such analytics.

We grabbed the data from Instagram photos using hashtag #tattoo from January to March 2015 and counted co-occurring hashtags. As simple as that. No deep learning image recognition involved, sorry ;-)

The analysis is part of our book «Nach dem Wochenende bin ich erstmal #krank» which was published in Germany, a collection of data charts about life on Instagram (http://a.co/i6aWkzQ). The design is by Ole Häntzschel, coding by David Goldwich and analysis by Tin Fischer.

Please note: Since Instagram restricted API access in July 2016 we unfortunately can no longer do such analytics.

We grabbed the data from Instagram photos using hashtag #rosen (German for roses; the result for #roses is similar) from 2013 to 2015. We only counted one picture per user (randomly chosen) to prevent heavy posters from distorting the result.

The analysis is part of our book «Nach dem Wochenende bin ich erstmal #krank» which was published in Germany in December, a collection of data charts about life on Instagram (http://a.co/i6aWkzQ). The design is by Ole Häntzschel (www.olehaentzschel.com), coding by David Goldwich and analysis by Tin Fischer (http://herrfischer.net).

Please note: Since Instagram restricted API access in July 2016 we unfortunately can no longer do such analytics.

We grabbed the meta data from Instagram photos using hashtag #backpacker from 2013 to 2015 and traced them. The result are 470 000 travel routes from 39 000 users. The map is clustered and the lines are brighter when over short distance and darker when long. That way you can see the routes more clearly. Hope you like it!

The map was created by my colleague David Goldwich using mainly Spatialite and QGIS. The analysis is part of our book «Nach dem Wochenende bin ich erstmal #krank» which was published in Germany in December 2016, a collection of data charts about life on Instagram (http://a.co/i6aWkzQ).

Please note: Since Instagram restricted API access in July 2016 we unfortunately can no longer do such analytics.

We got the coordinates from all kind of sources, depending on cities: Open Street Map, Wikipedia, transportation authority. And then there was a lot of manual work involved. The result was a table with all stations from which Jug drew the map, based on is INAT standard...